Modelling and Parameter Estimation of Dynamic Systems
1st Edition
Published on 1. November 2008
404 pages
978-1-84919-037-4 (ISBN)
Description
Parameter estimation is the process of using observations from a system to develop mathematical models that adequately represent the system dynamics. The assumed model consists of a finite set of parameters, the values of which are calculated using estimation techniques. Most of the techniques that exist are based on least-square minimisation of error between the model response and actual system response. However, with the proliferation of highspeed digital computers, elegant and innovative techniques like filter error method, genetic algorithms and artificial neural networks are finding more and more use in parameter estimation problems. Modelling and Parameter Estimation of Dynamic Systems presents a detailed examination of many estimation techniques and modelling problems.
Reviews / Votes
'while this book would be of interest to the general reader, it is of particular appeal to science and engineering students at all levels. It also presents a definite benefit to practising engineers and lecturers who are engaged in parameter estimation work. I found this an enthralling read, which now has a prominent place on my bookshelf - it certainly will be referred to in the future.' -- Karl O. Jones * Measurement & Control *More details
Series
Language
English
Place of publication
Stevenage
United Kingdom
Target group
College/higher education
Professional and scholarly
ISBN-13
978-1-84919-037-4 (9781849190374)
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Schweitzer Classification
Other editions
Additional editions
J.R. Raol | G. Dr. Girija | J. Singh
Modelling and Parameter Estimation of Dynamic Systems
Book
08/2004
Institution of Engineering and Technology
€170.50
Shipment within 3-4 weeks
Persons
J.R. Raol has B.E. and M.E. degrees in Electrical Engineering from M.S. University of Baroda, and a Ph.D. from McMaster University, Canada. He is a senior member of the IEEE (USA), life Fellow of the Aeronautical Society of India, life member of the System Society of India and senior scientist with National Aerospace Laboratories (NAL). Dr Raol has written around 100 publications and his research interests include multi-sensor data fusion, parameter estimation and artificial intelligence.
G. Girija has M.Sc. and Ph.D. degrees from Bangalore University and has written around 50 publications. Dr Girija currently works as a senior scientist at NAL, is a recipient of the NAL Foundation Day Award and is a life member of the Aeronautical Society of India. Her research areas are parameter estimation of aerospace vehicles and multi-sensor data fusion.
J. Singh has a Ph.D. in Aerospace Engineering from IIT Kanpur. He is a recipient of the Alexander von Humboldt Fellowship and has worked as a guest scientist at DLR-Institute for Flight Systems, Germany. He is a senior member of the AIAA and a life member of the Aeronautical Society of India and System Society of India. He currently works as a scientist at NAL and is a recipient of the NIL Foundation Day Award. His research areas are aircraft modelling and parameter estimation.
G. Girija has M.Sc. and Ph.D. degrees from Bangalore University and has written around 50 publications. Dr Girija currently works as a senior scientist at NAL, is a recipient of the NAL Foundation Day Award and is a life member of the Aeronautical Society of India. Her research areas are parameter estimation of aerospace vehicles and multi-sensor data fusion.
J. Singh has a Ph.D. in Aerospace Engineering from IIT Kanpur. He is a recipient of the Alexander von Humboldt Fellowship and has worked as a guest scientist at DLR-Institute for Flight Systems, Germany. He is a senior member of the AIAA and a life member of the Aeronautical Society of India and System Society of India. He currently works as a scientist at NAL and is a recipient of the NIL Foundation Day Award. His research areas are aircraft modelling and parameter estimation.
Content
Chapter 1: Introduction
Chapter 2: Least squares methods
Chapter 3: Output error method
Chapter 4: Filtering methods
Chapter 5: Filter error method
Chapter 6: Determination of model order and structure
Chapter 7: Estimation before modelling approach
Chapter 8: Approach based on the concept of model error
Chapter 9: Parameter estimation approaches for unstable/augmented
Chapter 10: Parameter estimation using artificial neural networks and genetic
Chapter 11: Real-time parameter estimation
Appendices
Chapter 2: Least squares methods
Chapter 3: Output error method
Chapter 4: Filtering methods
Chapter 5: Filter error method
Chapter 6: Determination of model order and structure
Chapter 7: Estimation before modelling approach
Chapter 8: Approach based on the concept of model error
Chapter 9: Parameter estimation approaches for unstable/augmented
Chapter 10: Parameter estimation using artificial neural networks and genetic
Chapter 11: Real-time parameter estimation
Appendices
